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scientifique

Integration of radar and optical Sentinel images for land use mapping in a complex landscape (case study: Arasbaran Protected Area) / Vahid Nasiri in Arabian Journal of Geosciences, vol 15 n° 24 (December 2022)  

Public [article]  inArabian Journal of Geosciences > vol 15 n° 24 (December 2022) . - n° 1759 Titre : Integration of radar and optical Sentinel images for land use mapping in a complex landscape (case study: Arasbaran Protected Area) Type de document : Article/Communication Auteurs : Vahid Nasiri, Auteur ; Arnaud Le Bris , Auteur ; Ali Asghar Darvishsefat, Auteur ; Fardin Moradi, Auteur Année de publication : 2022 Projets : 1-Pas de projet / Article en page(s) : n° 1759 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image mixte [Termes IGN] aire protégée [Termes IGN] carte d'occupation du sol [Termes IGN] classification par forêts d'arbres décisionnels [Termes IGN] classification par maximum de vraisemblance [Termes IGN] classification par séparateurs à vaste marge [Termes IGN] image Sentinel-MSI [Termes IGN] image Sentinel-SAR Résumé : (auteur) Considering the importance of accurate and up-to-date land use/cover (LULC) maps and in a situation of fast LULC changes, an accurate mapping of complex landscapes requires real-time high-resolution remote sensed data and powerful classification algorithms. The new ESA Copernicus satellites Sentinel-1 (S-1) and Sentinel-2 (S-2) have contributed to the effective monitoring of the Earth’s surface. This paper aims at assessing the potential of mono-temporal S-1 and S-2 satellite images and three common classification algorithms including maximum likelihood (ML), support vector machine (SVM), and random forest (RF) for LULC classification. The research methodology consists of a sequence of tasks including data collection and preprocessing, the extraction of texture and spectral features, the definition of several feature set configurations, classification, and accuracy assessment. Based on the results, using S-1 data alone leads to quite poor results, even though dual polarimetric C-band and texture features increased the classification accuracy. The S-2 data outperformed the S-1 data in terms of overall and class level accuracies. A combined use of S-1 and S-2 satellite images involving extracted features from both sources led to the best result for identifying all classes. This emphasizes the critical importance of using multi-modal datasets and different features in the LULC classification. Among classification algorithms, the SVM led to the highest accuracies irrespective of the dataset. To sum it up, according to the applied methodology and results, S-1 and S-2 data can provide optimal and up-to-date information for LULC mapping using non-parametric classifiers as SVM or RF. Numéro de notice : A2022-699 Affiliation des auteurs : UGE-LASTIG+Ext (2020- ) Thématique : IMAGERIE/INFORMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s12517-022-11035-z Date de publication en ligne : 07/12/2022 En ligne : https://doi.org/10.1007/s12517-022-11035-z Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102253 [article]

Estimating aboveground biomass in dense Hyrcanian forests by the use of Sentinel-2 data / Fardin Moradi in Forests, vol 13 n° 1 (January 2022)  

Public [article]  inForests > vol 13 n° 1 (January 2022) . - n° 104 Titre : Estimating aboveground biomass in dense Hyrcanian forests by the use of Sentinel-2 data Type de document : Article/Communication Auteurs : Fardin Moradi, Auteur ; Ali Asghar Darvishsefat, Auteur ; Manizheh Rajab Pourrahmati, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 104 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] biomasse aérienne [Termes IGN] Carpinus betulus [Termes IGN] classification barycentrique [Termes IGN] classification par forêts d'arbres décisionnels [Termes IGN] diamètre à hauteur de poitrine [Termes IGN] image Sentinel-MSI [Termes IGN] Iran [Termes IGN] régression multiple [Termes IGN] réseau neuronal artificiel Numéro de notice : A2022-080 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.3390/f13010104 Date de publication en ligne : 12/01/2022 En ligne : https://doi.org/10.3390/f13010104 Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99472 [article]

Multi-sensor aboveground biomass estimation in the broadleaved hyrcanian forest of Iran / Ghasem Ronoud in Canadian journal of remote sensing, vol 47 n° 6 ([01/11/2021])  

Public [article]  inCanadian journal of remote sensing > vol 47 n° 6 [01/11/2021] . - pp 818 - 834 Titre : Multi-sensor aboveground biomass estimation in the broadleaved hyrcanian forest of Iran Titre original : Estimation multi-capteurs de la biomasse aérienne de la forêt de feuillus hyrcanienne d’Iran Type de document : Article/Communication Auteurs : Ghasem Ronoud, Auteur ; Parviz Fatehi, Auteur ; Ali Asghar Darvishsefat, Auteur Année de publication : 2021 Article en page(s) : pp 818 - 834 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] biomasse aérienne [Termes IGN] classification barycentrique [Termes IGN] classification par forêts d'arbres décisionnels [Termes IGN] estimation statistique [Termes IGN] Fagus orientalis [Termes IGN] image Landsat-8 [Termes IGN] image Sentinel-MSI [Termes IGN] image Sentinel-SAR [Termes IGN] Iran [Termes IGN] régression multiple [Vedettes matières IGN] Inventaire forestier Mots-clés libres : Support Vector Regression Résumé : (auteur) In this study, the capability of Landsat-8 (L8), Sentinel-2 (S2), Sentinel-1 (S1), and their combination was investigated for estimating aboveground biomass (AGB). A pure stand of Fagus Orientalis located in the Hyrcanian forest of Iran was selected as the study area. The performance of a parametric approach, i.e., Multiple Linear Regression (MLR) model and non-parametric approaches, i.e., k-Nearest Neighbor (k-NN), Random Forest (RF), and Support Vector Regression (SVR), were also evaluated for AGB estimations. Our results indicated that among S2 metrics, the FAPAR canopy biophysical index and NDVI index based on the red-edge band (NIR-b8a) have the highest correlation coefficient (r) of 0.420 and 0.417, respectively. The results of AGB estimation showed that a combination of S2 and S1 datasets using the k-NN algorithm had the best accuracy (R2 of 0.57 and rRMSE of 14.68%). The best rRMSE using L8, S2, and S1 datasets was 18.95, 16.99, and 19.17% using k-NN, k-NN, and MLR algorithms, respectively. The combination of L8 with S1 dataset also improved the rRMSE relative to L8 and S1 separately by 0.96 and 1.18%, respectively. We concluded that the combination of optical data (L8 or S2) with SAR data (S1) improves the broadleaved Hyrcanian AGB estimation. Numéro de notice : A2021-956 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE/MATHEMATIQUE Nature : Article DOI : 10.1080/07038992.2021.1968811 Date de publication en ligne : 07/09/2021 En ligne : https://doi.org/10.1080/07038992.2021.1968811 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99982 [article]

Unmanned aerial vehicles (UAV)-based canopy height modeling under leaf-on and leaf-off conditions for determining tree height and crown diameter (Case study: Hyrcanian mixed forest) / Vahid Nasiri in Canadian Journal of Forest Research, Vol 51 n° 7 (July 2021)  

Public [article]  inCanadian Journal of Forest Research > Vol 51 n° 7 (July 2021) Titre : Unmanned aerial vehicles (UAV)-based canopy height modeling under leaf-on and leaf-off conditions for determining tree height and crown diameter (Case study: Hyrcanian mixed forest) Type de document : Article/Communication Auteurs : Vahid Nasiri, Auteur ; Ali Asghar Darvishsefat, Auteur ; Hossein Arefi, Auteur ; Marc Pierrot-Deseilligny , Auteur ; Manochehr Namiranian, Auteur ; Arnaud Le Bris , Auteur Année de publication : 2021 Projets : 1-Pas de projet / Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques [Termes IGN] diamètre des arbres [Termes IGN] filtre passe-bas [Termes IGN] hauteur des arbres [Termes IGN] image captée par drone [Termes IGN] modèle numérique de surface [Termes IGN] modèle numérique de surface de la canopée [Termes IGN] modèle numérique de terrain [Termes IGN] peuplement mélangé [Termes IGN] segmentation Résumé : (Auteur) Tree height and crown diameter are two common individual tree attributes that can be estimated from Unmanned Aerial Vehicles (UAVs) images thanks to photogrammetry and structure from motion. This research investigates the potential of low-cost UAV aerial images to estimate tree height and crown diameter. Two successful flights were carried out in two different seasons corresponding to leaf-off and leaf-on conditions to generate Digital Terrain Model (DTM) and Digital Surface Model (DSM), which were further employed in calculation of a Canopy Height Model (CHM). The CHM was used to estimate tree height using low pass and local maximum filters, and crown diameter was estimated based on an Invert Watershed Segmentation (IWS) algorithm. UAV-based tree height and crown diameter estimates were validated against field measurements and resulted in 3.22 m (10.1%) and 0.81 m (7.02%) RMSE, respectively. The results showed high agreement between our estimates and field measurements, with R2=0.808 for tree height and R2=0.923 for crown diameter. Generally, the accuracy of the results was considered acceptable and confirmed the usefulness of this approach for estimating tree heights and crown diameter. Numéro de notice : A2021-296 Affiliation des auteurs : UGE-LASTIG+Ext (2020- ) Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1139/cjfr-2020-0125 Date de publication en ligne : 26/01/2021 En ligne : https://dx.doi.org/10.1139/cjfr-2020-0125 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97376 [article]

Performance evaluation of land change simulation models using landscape metrics / Sadeq Dezhkam in Geocarto international, vol 32 n° 6 (June 2017)  

Public [article]  inGeocarto international > vol 32 n° 6 (June 2017) . - pp 655 - 677 Titre : Performance evaluation of land change simulation models using landscape metrics Type de document : Article/Communication Auteurs : Sadeq Dezhkam, Auteur ; Bahman Jabbarian Amiri, Auteur ; Ali Asghar Darvishsefat, Auteur ; Yousef Sakieh, Auteur Année de publication : 2017 Article en page(s) : pp 655 - 677 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] analyse diachronique [Termes IGN] champ aléatoire de Markov [Termes IGN] changement d'occupation du sol [Termes IGN] changement d'utilisation du sol [Termes IGN] géovisualisation [Termes IGN] Iran [Termes IGN] modèle de simulation [Termes IGN] simulation spatiale [Termes IGN] test de performance Résumé : (Auteur) This study proposes a landscape metrics-based method for model performance evaluation of land change simulation models. To quantify model performance at both landscape and class levels, a set of composition- and configuration-based metrics including number of patches, class area, landscape shape index, mean patch area and mean Euclidean nearest neighbour distance were employed. These landscape metrics provided detailed information on simulation success of a cellular automata-Markov chain (CA-Markov) model standpoint of spatial arrangement of the simulated map versus the corresponding reference layer. As a measure of model simulation success, mean relative error (MRE) of the metrics was calculated. At both landscape and class levels, the MRE values were accounted for 22.73 and 10.2%, respectively, which are further categorised into qualitative measurements of model simulation performance for simple and quick comparison of the results. Findings of the present study depict a hierarchical and multi spatial level assessment of model performance. Numéro de notice : A2017-276 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2016.1167967 Date de publication en ligne : 08/04/2016 En ligne : http://dx.doi.org/10.1080/10106049.2016.1167967 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=85305 [article]